With the advancement of increased computing power in embedded computing devices, face recognition applications are becoming more and more popular, e.g., Auto focus/Auto white balance/Auto exposure (3A) processing and smile shutter in digital cameras, avatar-based communications on smart phones, and face recognition login capabilities on handheld computing devices. In these facial analysis applications, facial landmark detection is an important processing step since the accuracy of the output results of a facial landmark detection module greatly affects the performance of succeeding facial image processing steps. In addition, facial landmark detection is one of the most time consuming modules in a face recognition processing pipeline. Therefore, fast facial landmark detection processing may be important for facial analysis applications, especially for embedded platforms with limited computing power (such as smart phones and mobile Internet devices (MIDs).
Recently, research into facial landmark detection techniques has increased. The main landmark points on a human face include eye corners, mouth corners, and nose tip. The detection task is to identify the accurate position of these points after the approximate region of a face is found. This is usually a nontrivial task, since there are significant variations of the appearance of facial features due to different head poses, facial expressions, uneven lightings, accessories, and potential occlusions. A good facial landmark detection process should be able to deal with all of these variations.
There are at least several known approaches, where the Active Shape Model (ASM) and the Active Appearance Model (AAM) are the most classical methods. These models are shown in “Statistical Models of Appearance for Computer Vision,” by T. F. Cootes and C. J. Taylor, University of Manchester, Mar. 8, 2004. The ASM/AAM use statistical methods to capture example variances in training sets and to optimize a cost function to fit a shape model to new examples. In recent years, improvements have been proposed within the ASM/AAM framework, such as utilizing advanced image features, or hierarchical coarse-to-fine searches. These methods improve the accuracy of landmark detection, but on the other hand, the computational cost grows significantly and it cannot reach real-time performance on modern embedded computing platforms. For example, one method as disclosed in “Robust Face Alignment Based on Hierarchical Classifier Network,” by Li Zhang, Haizhou Ai, and Shihong Lao, Proceedings of the European Conference on Computer Vision (ECCV) Workshop Human Computer Interface (HCI) 2006, pp. 1-11, is too slow for near real-time usage by known processing systems. Accordingly, better and more efficient methods of facial landmark detection processing are desired.